1 research outputs found
Joint Optimization of an Autoencoder for Clustering and Embedding
Incorporating k-means-like clustering techniques into (deep) autoencoders
constitutes an interesting idea as the clustering may exploit the learned
similarities in the embedding to compute a non-linear grouping of data at-hand.
Unfortunately, the resulting contributions are often limited by ad-hoc choices,
decoupled optimization problems and other issues. We present a
theoretically-driven deep clustering approach that does not suffer from these
limitations and allows for joint optimization of clustering and embedding. The
network in its simplest form is derived from a Gaussian mixture model and can
be incorporated seamlessly into deep autoencoders for state-of-the-art
performance